{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "from PIL import Image\n",
    "from dispnet_c import DispNet\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "# gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "# tf.config.experimental.set_memory_growth(gpus[0], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 800 images belonging to 1 classes.\n",
      "Found 800 images belonging to 1 classes.\n",
      "Found 800 images belonging to 1 classes.\n",
      "(4, 256, 512, 1) (4, 256, 512, 1) (4, 128, 256)\n"
     ]
    }
   ],
   "source": [
    "###########################读取driving数据\n",
    "path = \"/media/zouzhekang/C14D581BDA18EBFA/dataset/driving/frames_cleanpass/15mm_focallength/scene_forwards/slow\"\n",
    "pathd = \"/media/zouzhekang/C14D581BDA18EBFA/dataset/driving/disparity_img/15mm_focallength/scene_forwards/slow\"\n",
    "\n",
    "def trainGenerator(batch_size):\n",
    "\n",
    "    aug_dict = dict(horizontal_flip=True,\n",
    "                        fill_mode='nearest')\n",
    "    \n",
    "    left_datagen = ImageDataGenerator(**aug_dict)\n",
    "    right_datagen = ImageDataGenerator(**aug_dict)\n",
    "    deep_datagen = ImageDataGenerator(**aug_dict)\n",
    "    left_image = left_datagen.flow_from_directory(path,classes=['left'],color_mode = \"grayscale\",\n",
    "        target_size = (256, 512),class_mode = None,batch_size = batch_size, seed=3)\n",
    "    \n",
    "    right_image = right_datagen.flow_from_directory(path,classes=['right'],color_mode = \"grayscale\",\n",
    "        target_size = (256, 512),class_mode = None,batch_size = batch_size, seed=3)\n",
    "    \n",
    "    depth_image = deep_datagen.flow_from_directory(pathd,classes=['left'],color_mode = \"grayscale\",\n",
    "        target_size = (128, 256),class_mode = None,batch_size = batch_size, seed=3)\n",
    "\n",
    "    train_generator = zip(left_image, right_image, depth_image)\n",
    "    for (left,right,deep) in train_generator:\n",
    "        imgl = left/255.\n",
    "        imgr = right/255.  \n",
    "        deep = tf.squeeze(deep/255.)\n",
    "        yield (imgl,imgr,deep)\n",
    "        \n",
    "trainset = trainGenerator(batch_size=4)\n",
    "left_images,right_images,deep_images = next(trainset)\n",
    "print(left_images.shape,right_images.shape,deep_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SSIM(x, y):\n",
    "    C1 = 0.01 ** 2\n",
    "    C2 = 0.03 ** 2\n",
    "\n",
    "    mu_x = tf.nn.avg_pool2d(x, 3, 1, 'VALID')\n",
    "    mu_y = tf.nn.avg_pool2d(y, 3, 1, 'VALID')\n",
    "\n",
    "    sigma_x  = tf.nn.avg_pool2d(x ** 2, 3, 1, 'VALID') - mu_x ** 2\n",
    "    sigma_y  = tf.nn.avg_pool2d(y ** 2, 3, 1, 'VALID') - mu_y ** 2\n",
    "    sigma_xy = tf.nn.avg_pool2d(x * y , 3, 1, 'VALID') - mu_x * mu_y\n",
    "\n",
    "    SSIM_n = (2 * mu_x * mu_y + C1) * (2 * sigma_xy + C2)\n",
    "    SSIM_d = (mu_x ** 2 + mu_y ** 2 + C1) * (sigma_x + sigma_y + C2)\n",
    "    SSIM = SSIM_n / SSIM_d\n",
    "    return tf.clip_by_value((1 - SSIM) / 2, 0, 1)\n",
    "\n",
    "def do_primid(inp):\n",
    "    p1 = tf.nn.avg_pool2d(tf.expand_dims(inp,axis = 3),ksize = [1,2,2,1],strides=[1,2,2,1],padding = 'SAME')\n",
    "    p2 = tf.nn.avg_pool2d(tf.expand_dims(inp,axis = 3),ksize = [1,4,4,1],strides=[1,4,4,1],padding = 'SAME')\n",
    "    p3 = tf.nn.avg_pool2d(tf.expand_dims(inp,axis = 3),ksize = [1,8,8,1],strides=[1,8,8,1],padding = 'SAME')\n",
    "    p4 = tf.nn.avg_pool2d(tf.expand_dims(inp,axis = 3),ksize = [1,16,16,1],strides=[1,16,16,1],padding = 'SAME')\n",
    "    return tf.expand_dims(inp,axis=3),p1,p2,p3,p4 \n",
    "\n",
    "def get_loss(pred1,pred2,pred3,pred4,pred5,label):\n",
    "    label1,label2,label3,label4,label5 = do_primid(label)\n",
    "    loss1 = tf.reduce_mean(tf.square(label1 - pred1))\n",
    "    loss2 = tf.reduce_mean(tf.square(label2 - pred2))\n",
    "    loss3 = tf.reduce_mean(tf.square(label3 - pred3))\n",
    "    loss4 = tf.reduce_mean(tf.square(label4 - pred4))\n",
    "    loss5 = tf.reduce_mean(tf.square(label5 - pred5))\n",
    "    return loss1\n",
    "#     return (loss1/2 + loss2/4 + loss3/8 + loss4/16+ loss5/32)\n",
    "\n",
    "def get_ssim_loss(pred1,pred2,pred3,pred4,pred5,label):\n",
    "    label1,label2,label3,label4,label5 = do_primid(label)\n",
    "    loss1 = SSIM(label1,pred1)\n",
    "    loss2 = SSIM(label2,pred2)\n",
    "    loss3 = SSIM(label3,pred3)\n",
    "    loss4 = SSIM(label4,pred4)\n",
    "    loss5 = SSIM(label5,pred5)\n",
    "    loss1 = tf.reduce_mean(loss1)\n",
    "    loss2 = tf.reduce_mean(loss2)\n",
    "    loss3 = tf.reduce_mean(loss3)\n",
    "    loss4 = tf.reduce_mean(loss4)\n",
    "    loss5 = tf.reduce_mean(loss5)\n",
    "    return loss1\n",
    "#     return (loss1/2 + loss2/4 + loss3/8 + loss4/16+ loss5/32)\n",
    "\n",
    "model = DispNet()\n",
    "# model.build(input_shape=(None, 512, 1024, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/zouzhekang/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/math_grad.py:1394: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['disp_net/conv2d_3/kernel:0', 'disp_net/conv2d_3/bias:0', 'disp_net/conv2d_4/kernel:0', 'disp_net/conv2d_4/bias:0', 'disp_net/conv2d_5/kernel:0', 'disp_net/conv2d_5/bias:0'] when minimizing the loss.\n",
      "step: 0 loss: 0.40110546350479126\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['disp_net/conv2d_3/kernel:0', 'disp_net/conv2d_3/bias:0', 'disp_net/conv2d_4/kernel:0', 'disp_net/conv2d_4/bias:0', 'disp_net/conv2d_5/kernel:0', 'disp_net/conv2d_5/bias:0'] when minimizing the loss.\n",
      "step: 1 loss: 0.38207492232322693\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# model.load_weights(\"./weight\")\n",
    "optimizer = tf.keras.optimizers.Adam(lr = 5e-5)\n",
    "for epoch in range(1):\n",
    "    for step in range(2):\n",
    "        with tf.GradientTape() as tape:\n",
    "            left_images,right_images,deep_images = next(trainset)\n",
    "            pred1,pred2,pred3,pred4,pred5 = model(left_images,right_images)\n",
    "            loss = 0.85 * get_ssim_loss(pred1,pred2,pred3,pred4,pred5,deep_images) + 0.15 * get_loss(pred1,pred2,pred3,pred4,pred5,deep_images)\n",
    "            gradients = tape.gradient(loss, model.trainable_variables)\n",
    "            optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "            print(\"step:\",step,\"loss:\",float(loss))\n",
    "#     model.save_weights(\"./weight\")       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 128, 256, 1)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2304x9216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         真实↑                                                               预测↑\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x9216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         真实↑                                                               预测↑\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x9216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         真实↑                                                               预测↑\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x9216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         真实↑                                                               预测↑\n"
     ]
    }
   ],
   "source": [
    "# model.load_weights(\"./weight\")\n",
    "left_images,right_images,deep_images = next(trainset)\n",
    "pred1,pred2,pred3,pred4,pred5 = model(left_images,right_images)\n",
    "print(pred1.shape)\n",
    "out = tf.squeeze(pred1)\n",
    "for i in range(4):\n",
    "    fig = plt.figure(figsize = (32,128))\n",
    "    plt.imshow(tf.concat([deep_images[i],out[i]],axis = 1),cmap=\"hot\")\n",
    "    plt.show()\n",
    "    print(\"                         真实↑                                                               预测↑\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
